Relevant image detection in a camera, recorder, or video streaming device

ABSTRACT

The filtering tasks that are conventionally applied in a video monitoring application, to distinguish images that may be relevant to the application, are distributed to the image source, or near-source devices. Source devices, such as cameras and playback devices, and near-source devices, such as video concentrators and streaming devices, are configured to include video processing tools that can be used to pre-filter the image data to identify frames or segments of frames that include image information that is likely to be relevant to the receiving video monitoring application. In this manner, the receiving processor need not spend time and resources processing images that are pre-determined to be irrelevant to the receiving application.

This application claims the benefit of U.S. Provisional PatentApplication 60/668,446, filed 5 Apr. 2005.

BACKGROUND AND SUMMARY OF THE INVENTION

This invention relates to the field of video systems, and in particularto video input devices that include processes for identifying motion ina video image that is relevant to a given video processing task.

The application of video image processing to varieties of taskscontinues to grow. Such applications include, for example, videosurveillance, inventory control, traffic management, and so on.

FIG. 1 illustrates a typical image-based system. A video processor 150receives video image data from a variety of image sources 110, 120. Theimage source may be, for example, a video camera 110 that provides‘live’ images, or a video recorder 120 that provides previously capturedimages. The sources 110, 120 may be connected directly to the processor150, or via a network 130, or a combination of both.

The video processor 150 provides image information to anapplication/task 170 that performs a given function based on the imageinformation. The application 170 may be, for example, a surveillancesystem that processes the image information to identify situations thatwarrant an alarm; or, it may be an application that counts people orobjects that enter and exit portals; or, it may be a ‘passive’ systemthat merely stores select images for subsequent retrieval. Generallyspeaking, the application 170 identifies ‘events’ based on imageinformation, and initiates select action based on these events.

As the complexity of video monitoring systems increases, techniques havebeen developed to facilitate the efficient transmission of video images.U.S. Pat. No. 5,602,585, “METHOD AND SYSTEM FOR CAMERA WITH MOTIONDETECTION”, issued 11 Feb. 1997 to Dickinson et al., and incorporated byreference herein, teaches the use of a motion detector within a camerato selectively couple image data to a video processor, specifically, avideo recorder. The camera is initially placed in a differential mode,wherein changes to the image are provided to the motion detector; whenthe amount of changes/motion exceeds a given threshold, the camera isplaced in full-video mode, wherein the camera is coupled to therecorder, and full images are provided from the camera. After apredetermined duration, the camera is again placed in the differentialmode, and decoupled from the recorder.

FIG. 1B illustrates a block diagram of a Dickinson-like technique in thecontext of this invention. The camera 110 includes a video capturecomponent 112 that sequentially captures images, and a motion detector116 that determines whether the amount of change/motion in the sequenceexceeds a given threshold. The motion detector controls a switch 114that selectively couples the video images from the video capturecomponent 112 to the output of the camera, based on whether thethreshold is exceeded. In this manner, only images that exhibit at leasta minimum amount of change/motion are communicated to the videoprocessor 150. This technique is particularly effective for minimizingtraffic on a limited bandwidth video network that may be coupled to aplurality of video sources, such as illustrated in FIG. 1A. That is, ifeach of the cameras 110 and the DVR 120 of FIG. 1A are configured toonly transmit changes that exceed a given threshold to the processor150, the bandwidth of the channel used to route the video from eachsource 110, 120 to the video processor can be substantially reduced,compared to a continuous video stream from each of the sources 110, 120.

As digital processing techniques advance, the need for a Dickinson-liketechnique to minimize bandwidth requirements is diminished, asillustrated in FIG. 1C. In this example system, the camera 110 includesan MPEG encoder 118, so that the output stream from the camera 110 is anMPEG-encoded stream. As is known in the art, the MPEG format isinherently a differential format, wherein only the changes to regularlycommunicated reference images are transmitted. As such, if there is nochange between images, no additional ‘change frames’ need becommunicated. Further, the bandwidth used to communicate eachchange-frame will be dependent upon the amount of change. That is, minorchanges consume minor amounts of bandwidth, whereas fuller or morecomplex changes consume substantially more bandwidth. One of ordinaryskill in the art will recognize that Dickinson's threshold-based gatingcould also be applied to the system of FIG. 1C, although the relativeincrease in efficiency, compared to its application to full-stream videowould be substantially decreased.

Returning to FIG. 1A, as video monitoring systems increase incomplexity, the ‘scalability’ of the video processor 150 and videoapplication 170 becomes a limiting factor in the expansion of the videomonitoring capabilities to include multiple video sources 110, 120. Evenwith the use of motion-only filtering, as illustrated in FIG. 1B, ordifferential imaging, as illustrated in FIG. 1C, the video processor 150and/or the application 170 are still required to process each frame fromeach source 110, 120 that reports motion, and, in the case of FIG. 1C,this processing necessarily includes decoding the received MPEG framesto produce the image frames.

A further problem with the motion-based filtering approaches of FIGS. 1Band 1C relates to the indiscriminate nature of motion-detection. In anoutdoor scene, for example, the random movement of leaves and branchesof a tree can produce a measure of perceived motion that equals orexceeds the measure of motion of a person entering or leaving a scene.In an indoor scene, movements in ‘permitted’ areas, such as the areanear bank tellers produce a measure of motion that is indistinguishablefrom a measure of motion produced by movements in ‘protected’ areas,such as the area near the bank's safe. That is, conventionalmotion-based filtering techniques are fairly ineffective in environmentsthat are expected to exhibit movements that are irrelevant to the taskat hand, and are generally only effective in limited environments, suchas systems that monitor the interior of bank safes, or office or factoryenvironments during ‘off-hours’, and so on.

An object of this invention is to provide a video monitoring system thatis well structured for multiple-camera operations. A further object ofthis invention is to provide a video monitoring system that is wellsuited for environments that exhibit activity/motion that is generallyunrelated to the video monitoring application. A further object of thisinvention is to provide a video monitoring system that reduces theamount of video processing or video analysis required to perform a giventask. A further object of this invention is to further reduce thebandwidth requirements for video monitoring systems.

These objects, and others, are achieved by distributing the videoprocessing typically performed in a video monitoring system among thecomponents of the system. Specifically, the filtering tasks that areconventionally applied in a video monitoring application, to identifyactivity in the images that may be relevant to the monitoring task, aredistributed to the image source, or near-source devices. Source devices,such as cameras and playback devices, and near-source devices, such asvideo concentrators and streaming devices, are configured to includevideo processing tools that can be used to pre-filter the image data toidentify frames or segments of frames that include information that islikely to be relevant to the receiving video monitoring application. Inthis manner, the receiving processor need not spend time and resourcesprocessing images that are pre-determined to be irrelevant to thereceiving application.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is explained in further detail, and by way of example,with reference to the accompanying drawings wherein:

FIGS. 1A-1C illustrates an example block diagram of prior art videomonitoring systems.

FIG. 2 illustrates an example block diagram of an embodiment of a videomonitoring system in accordance with this invention.

FIGS. 3A-3C illustrate other example block diagrams of embodiments of avideo monitoring system in accordance with this invention.

FIG. 4 illustrates an example block diagram of a hybrid embodiment thatincludes conventional video devices, and video devices in accordancewith this invention.

Throughout the drawings, the same reference numerals indicate similar orcorresponding features or functions. The drawings are included forillustrative purposes and are not intended to limit the scope of theinvention.

DETAILED DESCRIPTION

In the following description, for purposes of explanation rather thanlimitation, specific details are set forth such as the particulararchitecture, interfaces, techniques, etc., in order to provide athorough understanding of the concepts of the invention. However, itwill be apparent to those skilled in the art that the present inventionmay be practiced in other embodiments, which depart from these specificdetails. In like manner, the text of this description is directed to theexample embodiments as illustrated in the Figures, and is not intendedto limit the claimed invention beyond the limits expressly included inthe claims. For purposes of simplicity and clarity, detaileddescriptions of well-known devices, circuits, and methods are omitted soas not to obscure the description of the present invention withunnecessary detail. In like manner, the examples are provided usinglabels and terminology that are easily recognized, to facilitateunderstanding. For example, the terms “MPEG”, “NTSC”, and “PAL” are usedas paradigms for digital and analog encoding technologies, although theprinciples of this invention are not limited to these examples.Similarly, the use of a particular term, such as MPEG, is intended toinclude any and all of the derivatives and variations, such as MPEG1,MPEG2, MPEG4, MJPEG, H.263, H.264, and so on.

With advances in technology and miniaturization, video source devices,such as digital cameras for consumer use, are being provided withadvanced video processing capabilities, including, for example, imagestabilization, white level balancing, and so on. This invention ispremised on the observation that this same technology can be applied forother video processing tasks, such as distinguishing images that may berelevant or irrelevant to a given video processing application. Whensuch tasks are performed at the source devices, the video processingapplication can exhibit a substantial increase in performance. Consider,for example, an application that conventionally spends half its timeidentifying images of interest, and half its time determining whether anactionable event is indicated in the identified images of interest. Aten camera system in a conventional system incurs a 10× increase inprocessing time, while a ten camera system with cameras that identifyimages of interest will only incur a 5× increase in processing time. Afurther increase in performance will be achieved if the applicationspends a larger proportion of time identifying images of interest, as istypically the case.

FIG. 2 illustrates an example embodiment of a camera 210 in accordancewith this invention. In this embodiment, the camera 210 includes animage detector 260 that is configured to filter the image data capturedby the video capture component 112, to identify images that arepotentially relevant to the video application 270. More specifically,the image detector 260 applies one or more filters that are configuredto eliminate images that are determined to be irrelevant to application270 from further consideration. If an image is not eliminated as beingirrelevant, it is identified as being potentially relevant. Thisidentification of potentially relevant images is communicated to thevideo processor 250 and application 270; either or both of thesecomponents 250, 270 can thereafter ignore any images that are notidentified as being potentially relevant to the application 270. In thismanner, the resources of the processor 250 and application 270 can befocused on application-specific tasks, such as determining whether theimage indicates that an alarm should be sounded in a surveillancesystem, whether a count should be incremented in an asset controlsystem, whether a traffic light sequence should be changed in a trafficcontrol system, and so on.

In accordance with this invention, the relevant-image detector 260 isconfigured to filter the images based on one or more imagecharacteristics, and not merely whether a threshold amount ofchange/movement occurs in the image. That is, for example, the fact thata given number of pixels have changed, or the fact that a magnitude of acumulative measure of change/movement occurs in the image provides noinformation concerning the content of the image. Conversely, the factthat, for example, a contiguous cluster of pixels exhibits a changewhich indicates that the image may contain an object corresponding tothis contiguous cluster. In like manner, the fact that a contiguouscluster of pixels of a given size exhibits a flesh tone indicates thatthe image may contain a facial image. In general terms, a relevant-imagedetector is configured to determine whether the image containscharacteristics associated with one or more target objects, such assize, shape, color, texture, velocity, orientation, rigidity, height,altitude, and so on.

The following are examples of techniques for identifying relevantimages. U.S. Pat. No. 4,931,868, “METHOD AND APPARATUS FOR DETECTINGINNOVATIONS IN A SCENE”, issued 5 Jun. 1990 to Ivan Kadar, andincorporated by reference herein, assesses changes to groups of pixels,and is able to distinguish motions of objects from random changes inpixel values, and is also able to identify changes of texture in ascene, even when there is no actual object movement in the scene. U.S.Pat. No. 4,249,207, “PERIMETER SURVEILLANCE SYSTEM”, issued 3 Feb. 1981to Harman et al., and incorporated herein, partitions an image intovariable sized cells, corresponding to an area that a person wouldoccupy in the image, depending upon the distance of the imaged regionfrom the camera. Each cell is subsequently assessed to determine whetherthe image contains a person-sized object within the cell. U.S. Pat. No.6,130,707, “VIDEO MOTION DETECTOR WITH GLOBAL INSENSITIVITY”, issued 10Oct. 2000 to Koller et al., and incorporated by reference herein,determines an overall measure of changes in an image, such as caused bya change of illumination, and uses this measure to adjust a thresholdvalue that is used to distinguish changes in object-size sub-areas ofthe image. U.S. Pat. No. 5,721,692, “MOVING OBJECT DETECTION APPARATUS”,issued 24 Feb. 1998 to Nagaya et al., and incorporated by referenceherein, detects the direction and velocity of objects in an image bypartitioning the image into narrow slits and monitoring the change ofintensity of the slit over time, to identify objects passing through theslit. USPA 2004/0155958, “USER ASSISTED CUSTOMIZATION OF AUTOMATED VIDEOSURVEILLANCE SYSTEMS”, filed 9 May 2003 for Mi-Suen Lee, andincorporated by reference herein, allows a user to define a variety ofpossible target objects, such as people, vehicles, and so on, andassociated sets of characteristics associated with each target type.

In addition to, or in lieu of, these object-characteristic filteringtechniques, the relevant image detector 260 may also include filtersthat distinguish relevant from irrelevant images based on where in theimage an object or activity occurs. U.S. Pat. No. 6,727,938, “SECURITYSYSTEM WITH MASKABLE MOTION DETECTION AND CAMERA WITH AN ADJUSTABLEFIELD OF VIEW”, issued 27 Apr. 2004 to Jennifer L. Randall, andincorporated by reference herein, teaches the use of one or more masksto block regions of an image within which the occurrence of activity isirrelevant to the application. USPA 2005/0157169, “OBJECT BLOCKING ZONESTO REDUCE FALSE ALARMS IN VIDEO SURVEILLANCE SYSTEMS”, filed 20 Oct.2004 for Brodsky et al., and incorporated by reference herein, teachesfiltering the occurrence activities that originate within definedregions, but not filtering the activities/objects that traverse theregions.

Many other techniques are commonly used to identify or filter imagesbased on characteristics of the image and include, for example,techniques that distinguish/filter reflections from actual objects, astaught, for example in USPA 2005/0058323, “SYSTEM AND METHOD FORCOUNTING CARS AT NIGHT”, filed 5 Feb. 2004 for Tomas Brodsky, andincorporated by reference herein.

FIG. 2 illustrates that the relevant image detector 260 providing itsoutput, the identification of relevant images, along a separatecommunication path to the video processor 250. One of ordinary skill inthe art will recognize that the identification of relevant images couldalso be communicated on the same channel as the video data, using any ofa variety of multiplexing and/or encoding techniques. Optionally, thedetector 260 may be configured to send a separate mask that identifiesthe region or regions in the image that caused the image to bedetermined to be relevant, or an identification of such a region orregions, such as the coordinates of a bounding box that includes theregions upon which the relevance determination was based. The detector260 may also be configured to initiate the encoding and/or communicationof alternative planes of the image that may facilitate the furtherprocessing and analysis of the relevant images. In like manner, if thesource device 210 or the processor 250 includes an image memory, theoccurrence of a relevant image may also trigger the playback orrecording of images preceding the relevant image, to facilitate adetermination of the cause of the relevant image.

The video processor 250 and application 270 are configured to processthe images that are identified by the source device 210 as beingpotentially relevant to the application 270. The processor 250 may beconfigured for example, to record all of the received images, or onlythose identified as being potentially relevant, or it may be configuredto record all of the received images along with all of the receivedindications of whether each image is potentially relevant. The processor250 may also be configured to record all images, but at differingresolutions, depending upon the relevancy determination. Similarly, theprocessor 250 may be configured to decode and provide potentiallyrelevant images to the application 270, or it may provide all receivedimages to the application 270, and the application 270 can be configuredto only process the relevant images. The processor 250 and application270 may also be configured to process ‘reference’ images and the like,as required, regardless of their relevant/irrelevant classification, toenable the processing of subsequent relevant image frames that rely onthese reference images.

Depending upon the amount and type of relevance-filtering applied at thesource 210, any of a variety of subsequent actions and processes mayoccur at the processor 250 and/or source 270. In a straightforwardapplication wherein all of the target-determining filtering is performedat the source device 210, the application 270 may merely be configuredto provide an alarm notification upon receipt of a relevant image. Inother embodiments, the application 270 may be configured to applyadditional filtering to determine whether the image, or images, that areidentified as being potentially relevant include alarm-producingactivities. In a preferred embodiment, such alarm indications include anindication of where, in the image or in real space, the alarm-producingactivity is occurring.

Additionally, the alarm indication may include messages or commands thatare communicated to the source device 210 or other source devices, tooptimize the information-gathering and/or filtering tasks. For example,one or more of the source devices may be controlled to pan, tilt, orzoom to provide a better view of the area of activity. In an imagetracking application, the application 270 may identify a distinguishingfeature in an object of interest, and communicate directives to therelevant-image detectors 260 in one or more of the source devices tofurther filter the images. That is, for example, if the detector 260 inthe original source device identifies a person with a red hat as atarget object, detectors 260 in other source devices may be configuredto identify only objects with red hat characteristics (e.g. a set ofpredominantly red pixels at an upper region of a set of object pixels)as relevant, or, to add a further indication, such as ‘very relevant’ toany identified relevant image that also include objects with red hatcharacteristics.

Conceptually, the relevant image detector 260 and application 270 form ahierarchy of filtering and/or reasoning engines, wherein the relevantimage detector applies image-reasoning techniques to distinguishrelevant images, and the application 270 applies event-reasoningtechniques to distinguish events requiring subsequent actions.Obviously, the amount of image-reasoning that can be applied at thedetector 260 is dependent upon the resources available at the source210, and any further image-reasoning that is required will be performedat the processor 250 and/or the application 270.

Preferably, the relevant image detector 260 contains a core group oftarget identifying modules with programmable parameters, such as a sizeor shape module that can be programmed to identify images that containmoving objects of at least a minimum given size or shape relevant, theminimum size and shape being dependent upon the particular application270. For example, a vehicular traffic control application would specifya larger minimum size for potential target objects than a luggageconveyance system's minimum size target object. A combination ofparameters may also be provided, such as height, length, or areaparameters, along with a definition of the Boolean operations (and, or,not, greater-than, less-than, etc.) to be applied to these parameters toidentify relevant or irrelevant images. In a more sophisticatedembodiment, the relevant image detector 260 may include a more powerfuland/or specialized processor, such as a digital signal processor (DSP),that can be programmed for executing other algorithms, such as thosediscussed above, for identifying objects, recognizing features, maskingregions of the image, and so on. The detector 260 may be dynamicallyreprogrammed or reconfigured, based on ongoing activities, as discussedabove with regard to the ‘look for a person with a red hat’ example.Depending upon the particular embodiment, the relevant image detector260 may be preconfigured with common algorithms and default parametersthat are selectively enabled when the video monitoring system isinstalled, and/or, the parameter values, custom programs, and enablementsettings may be programmed during a set-up or maintenance procedureafter installation, and/or, the operator of the monitoring system may beprovided with programming, enabling, and parameter setting options on acontinuing or as-needed basis.

FIGS. 3A-3C illustrate a variety of other configurations of sourcedevices in accordance with this invention. In each of these examples, asin the above example, particular features or options are presented inthe context of the particular example. However, one of ordinary skill inthe art will recognize that such features are not necessarily limited tothe particular embodiment that is used to provide context, and may beapplied to other embodiments as well.

As illustrated in FIG. 3A, the source device 320 includes an imagedetector 260 that is configured to control the MPEG encoder 118, orother transmission component, to inhibit the encoding/transmission ofirrelevant images, thereby implicitly communicating therelevant/irrelevant determination from the relevant image detector 260by only communicating potentially relevant images 301. Optionally, thedetector 260 may also be configured to provide an explicit indication322 as well, or ancillary information such as the aforementioned ‘veryrelevant’ indication when particular features or activities aredetected.

Additionally, or alternatively, the image detector 260 may be configuredto control the type of information that is communicated based on therelevant/irrelevant determination. For example, the detector 260 may beconfigured to direct the encoder 118 to send a full image referenceframe when a relevant image is first detected, to assure that thereceiving system is in-sync for subsequent images. Similarly, it maycontrol the encoder 118 to modify the encoding parameters, such as theframe rate or resolution, or it may add ancillary information to thecontent of the output images 301, including, for example, an indicationof the segment of the image that triggered the relevancy determination,such as a bounding box. Other controls and modifications to the encodingof images based on a determination of the potential relevancy of theimages will be evident to one of ordinary skill in the art in view ofthis disclosure.

FIG. 3B illustrates a source device 330 having a similar configurationto device 320 in FIG. 3A, except that the output images 302 are encodedusing an analog encoder 119, such as an NTSC or PAL encoder. Thedetector 260 may directly control the encoder to enable or disableencodings, or add ancillary information to the encoding, using, forexample, techniques similar to those used for adding closed-captionoverlays or embedded teletext. As illustrated, an explicit indication332 may also be provided, to facilitate the processing of the identifiedrelevant images.

In an embodiment of an encoding and/or transmission controlling use ofthe image detector 260, such as illustrated in FIGS. 3A and 3B, theimage detector 260 or its associated control element is preferablyconfigured to enable the continuous encoding/transmission of images fora given period of time after the detection of a relevant image. In thismanner, the receiving system receives subsequent images regardless ofwhether the relevance-indicating conditions continue to be exhibited inthe subsequent images, thereby ongoing images to facilitate an ongoingassessment of the situation.

FIG. 3C illustrates an embodiment of this invention in a videoconcentrator or video streamer 340 that is configured to provide aninterface between one or more source devices 110, 120 and acommunications network 390, such as a telephone network, an Internetnetwork, a local network, a private network, a point-to-point network,and so on. The streamer 340 includes a receiver 342 that is configuredto receive images from one or more of the sources 110, 120, and anencoder/transmitter 344 that is configured to forward the images to asubsequent video processing system 250, 270 via the network 390. In thisembodiment, a relevant image detector 260 processes the images from theone or more sources 110, 120 to identify potentially relevant images,using one or more of the techniques discussed above, or other imagecharacterization/reasoning techniques available in the art. As in theprevious examples, the detector 260 may merely communicate its relevancydeterminations, or it may use these determinations to control one ormore aspects of the video streamer 340, or a combination of both. In apreferred embodiment, to optimize bandwidth utilization, the detector260 is configured to control the encoding/transmission 344 of the imagesbased on the relevancy determination, either by enabling or disablingthe transmission of each image, or by controlling one or more of theencoding parameters, such as the frame rate, resolution, or others.

The streamer 340 is also preferably configured to optionally record theimages, using, for example, a video DVR 120 or other storage device. Ina preferred embodiment, the relevancy determination from the detector260 is also used to control the recording of the images, either byenabling or disabling the recording of each image, or by controlling oneor more of the encoding parameters, such as the frame rate, resolution,or others. As would be evident to one of ordinary skill in the art, thefunctionality of the streamer 340 may be embedded in such a DVR, orother recording device, thereby eliminating the need to provide aseparate component to perform the streamer-with-RID processing.

FIG. 4 illustrates a hybrid configuration of conventional devices andrelevant-image-detecting devices in accordance with this invention, asmight be embodied in a video recording or video streaming device 360. Inthis embodiment, the device 360 is configured to receive images fromeither conventional image sources 110 or from image sources 210 thatinclude a relevant image detector (RID). A relevant image detector 260is configured to distinguish potentially relevant images within thereceived images, in combination with the information provided by any ofthe remote RIDs. If the remote RID in the source 210 and the localdetector 260 are configured to apply the same criteria for identifyingrelevant images, the local detector 260 does not process the images fromthe source 210, and merely acts as a conduit for the relevancyinformation, and uses the relevancy determination in the same manner asit would had the relevancy been determined by the detector 260. If thelocal detector 260 applies a higher level of filtering than the remoteRID at the source 210, the detector 260 is configured to only processthe images from the source 210 that have been identified as beingpotentially relevant by the lower level filters at the remote RID. Ifthe local detector 260 and remote RID at the source 210 have disjointfiltering criteria, the local detector 260 processes the images from thesource 210 in the same manner as it does the images from conventionalsources 110, and optionally communicates or records both relevancydeterminations to facilitate further processing. In a preferredembodiment, the device 360 includes a user interface that allows a userto establish the relationships between the remote and local filters(same, hierarchical, disjoint), and to establish the reporting scheme(one, both, and, or) for disjoint relevancy determinations.

By providing hybrid and/or hierarchical configurations, the use of theprinciples of this invention can provide solutions that easily scale toaccommodate large and complex multi-camera video-monitoring systems.

The foregoing merely illustrates the principles of the invention. Itwill thus be appreciated that those skilled in the art will be able todevise various arrangements which, although not explicitly described orshown herein, embody the principles of the invention and are thus withinthe spirit and scope of the following claims.

In interpreting these claims, it should be understood that:

a) the word “comprising” does not exclude the presence of other elementsor acts than those listed in a given claim;

b) the word “a” or “an” preceding an element does not exclude thepresence of a plurality of such elements;

c) any reference signs in the claims do not limit their scope;

d) several “means” may be represented by the same item or hardware orsoftware implemented structure or function;

e) each of the disclosed elements may be comprised of hardware portions(e.g., including discrete and integrated electronic circuitry), softwareportions (e.g., computer programming), and any combination thereof;

f) hardware portions may be comprised of one or both of analog anddigital portions;

g) any of the disclosed devices or portions thereof may be combinedtogether or separated into further portions unless specifically statedotherwise;

h) no specific sequence of acts is intended to be required unlessspecifically indicated; and

i) the term “plurality of” an element includes two or more of theclaimed element, and does not imply any particular range of number ofelements; that is, a plurality of elements can be as few as twoelements, and can include an immeasurable number of elements.

We claim:
 1. A video monitoring system comprising: a video captureelement that is configured to provide a plurality of video images, and acorresponding image detector that is configured to: receive the videoimages, and determine a relevancy of each image by filtering each imagebased on one or more image characteristics of the image; and a remotevideo processing system that receives the plurality of images and therelevancy of each image from the corresponding image detector, theremote video processing system is configured to process the plurality ofimages based on the determined relevancy of each image and todynamically reprogram or reconfigure the corresponding image detector toidentify a distinguishing feature by further filtering an object ofinterest detected by the remote video processing system.
 2. The cameraof claim 1, including an image encoder that is configured to communicatean encoding of the video images to a receiving system that includes thevideo monitoring application.
 3. The camera of claim 2, wherein theimage encoder includes a digital video encoder that is configured toprovide differential image encoding.
 4. The camera of claim 2, whereinthe image encoder includes at least one of: an NTSC encoder and a PALencoder.
 5. The camera of claim 2, wherein the relevant image detectoris configured to control one or more aspects of the image encoder. 6.The camera of claim 2, wherein the relevant image detector is configuredto provide an indication of the relevancy of each image, and the imageencoder is configured to communicate the indication to the receivingsystem.
 7. The camera of claim 2, wherein the relevant image detector isconfigured to communicate an indication of the relevancy to thereceiving system.
 8. The camera of claim 1, wherein the one or moreimage characteristics include at least one of: a size, a shape, a color,a height, and an altitude.
 9. The camera of claim 8, wherein the one ormore image characteristics include at least one of: a texture, avelocity, an orientation, a rigidity, a length, and a height.
 10. Thecamera of claim 1, wherein the relevant image detector is configured toreceive parameter values corresponding to the one or more imagecharacteristics that facilitate determining the relevancy.
 11. Thecamera of claim 10, wherein the relevant image detector is configured totransmit other parameter values to other relevant image detectors tofacilitate determining subsequent relevancies in other images.
 12. Thecamera of claim 1, wherein the relevant image detector is configured todetermine the relevancy based on changes at each of at least apredefined number of contiguous pixels.
 13. The camera of claim 1,wherein the relevant image detector is configured to determine therelevancy based on changes at each of a predefined arrangement ofcontiguous pixels.
 14. The camera of claim 1, wherein the relevant imagedetector is configured to determine the relevancy based on changeswithin each of a plurality of regions within each image.
 15. The cameraof claim 1, wherein the relevant image detector is configured todetermine the relevancy based on changes within at least one region ofeach image that is smaller in area than the image.
 16. The camera ofclaim 1, including a recorder that is configured to record the videoimages.
 17. The camera of claim 16, wherein the relevant image detectoris configured to control one or more parameters of the recorder based onthe relevancy.
 18. The camera of claim 16, wherein the relevant imagedetector is configured to selectively enable the recorder based on therelevancy.
 19. The camera of claim 1, wherein the relevant imagedetector includes a programmable processor that is configured tofacilitate modification of the one or more image characteristics. 20.The camera of claim 1, wherein the relevant image detector includes aprogrammable processor that is configured to facilitate modification ofa program used to determine the relevancy.
 21. The camera of claim 20,wherein the programmable processor includes a digital signal processor.22. A video streaming system that includes: a receiver that isconfigured to receive video images from one or more video sources, anencoder that is configured to transmit the video images to a receivingsystem that includes a remote video monitoring application, and arelevant image detector that is configured to: receive the video images,and determine a relevancy of each image by filtering each image based onone or more image characteristics of the image, based on one or morecharacteristics associated with target objects of the video monitoringapplication; and the remote video monitoring application that receivesthe plurality of images and the relevancy of each image from therelevant image detector, the remote video monitoring application isconfigured to process the plurality of images based on the relevancy ofeach image and to dynamically reprogram or reconfigure the relevantimage detector to identify a distinguishing feature by further filteringof target objects detected by the remote video monitoring application.23. The system of claim 22, wherein the relevant image detector includesa programmable processor that is configured to facilitate modificationof the one or more image characteristics.
 24. The system of claim 22,wherein the relevant image detector includes a programmable processorthat is configured to facilitate modification of a program used todetermine the relevancy.
 25. The system of claim 24, wherein theprogrammable processor includes a digital signal processor.
 26. Thesystem of claim 22, wherein the one or more image characteristicsinclude at least one of: a size, a shape, a color, a height, and analtitude.
 27. The system of claim 26, wherein the one or more imagecharacteristics include at least one of: a texture, a velocity, anorientation, a rigidity, a length, and a height.
 28. The system of claim22, wherein the encoder includes a digital video encoder that isconfigured to provide differential image encoding.
 29. The system ofclaim 22, wherein the encoder includes at least one of: an NTSC encoderand a PAL encoder.
 30. The system of claim 22, wherein the relevantimage detector is configured to control one or more aspects of theencoder.
 31. The system of claim 22, wherein the relevant image detectoris configured to provide an indication of the relevancy of each image,and the encoder is configured to communicate the indication to thereceiving system.
 32. The system of claim 22, wherein the relevant imagedetector is configured to receive parameter values corresponding to theone or more image characteristics that facilitate determining therelevancy.
 33. The system of claim 32, wherein the relevant imagedetector is configured to transmit other parameter values to otherrelevant image detectors to facilitate determining subsequentrelevancies in other images.
 34. The system of claim 22, wherein therelevant image detector is configured to determine the relevancy basedon changes at each of at least a predefined number of contiguous pixels.35. The system of claim 34, wherein the relevant image detector isconfigured to determine the relevancy based on changes at each of apredefined arrangement of contiguous pixels.
 36. The system of claim 22,wherein the relevant image detector is configured to determine therelevancy based on changes within each of a plurality of regions withineach image.
 37. The system of claim 22, wherein the relevant imagedetector is configured to determine the relevancy based on changeswithin at least one region of each image that is smaller in area thanthe image.
 38. The system of claim 22, including a recorder that isconfigured to record the video images.
 39. The system of claim 38,wherein the relevant image detector is configured to control one or moreparameters of the recorder based on the relevancy.
 40. The system ofclaim 38, wherein the relevant image detector is configured toselectively enable the recorder based on the relevancy.
 41. A methodincluding: receiving a plurality of images from a video capture element;filtering each of the plurality of images based on one or more imagecharacteristics of the image; identifying irrelevant images from theplurality of images based on the filtering; identifying a relevancy ofeach of the plurality of images based on the identification ofirrelevant images; processing a plurality of images to identify arelevancy of each image based on characteristics associated with targetobjects of a video monitoring application, communicating the pluralityof images and the relevancy of each image to a remote video processingsystem that is configured to process the images of the plurality ofimages based on the relevancy of each image; and the remote videoprocessing system dynamically reprogramming or reconfiguring therelevant image detector to identify a distinguishing feature by furtherfiltering an object of interest detected by the remote video processingsystem.
 42. The method of claim 41, including processing the images ofthe plurality of images based on the relevancy of each image.
 43. Themethod of claim 41, wherein communicating the plurality of imagesincludes encoding the plurality of images based on the relevancy of oneor more of the images.
 44. The method of claim 43, wherein encoding theimages includes adjusting at least one of the image frame rate and theimage resolution based on the relevancy of the one or more images. 45.The method of claim 41, wherein the one or more image characteristicsinclude at least one of: a size, a shape, a color, a height, and analtitude.
 46. The method of claim 41, including receiving parametervalues corresponding to the one or more image characteristics thatfacilitate determining the relevancy.
 47. The method of claim 41,including recording the images based on the relevancy.